Efficacy of Initial Antiretroviral Therapy for HIV-1 Infection in Adults: A Systematic Review and MetaAnalysis of 114 Studies with up to 144 Weeks’ Follow-Up Frederick J. Lee1*, Janaki Amin2, Andrew Carr1,3 1 Clinical Research Program, St. Vincent’s Centre for Applied Medical Research, Sydney, Australia, 2 The Kirby Institute, University of New South Wales, Sydney, Australia, 3 HIV, Immunology and Infectious Diseases Unit, St. Vincent’s Hospital, Sydney, Australia
Abstract Background: A comprehensive assessment of initial HIV-1 treatment success may inform study design and treatment guidelines. Methods: Group-based, systematic review and meta-analysis of initial antiretroviral therapy studies, in adults, of $48 weeks duration, reported through December 31, 2012. Size-weighted, intention-to-treat efficacy was calculated. Parameters of study design/eligibility, participant and treatment characteristics were abstracted. Multivariable, random effects, linear regression models with backwards, stepwise selection were then used to identify variables associated with efficacy. Outcome Measures: Antiviral efficacy (undetectable plasma viral load) and premature cessation of therapy. Results: 114 studies were included (216 treatment groups; 40,124 participants; mean CD4 count 248 cells/mL [SD 81]; mean HIV-1 plasma viral load log10 4.9 [SD 0.2]). Mean efficacy across all groups was 60% (SD 16) over a mean 82 weeks’ follow-up (SD 38). Efficacy declined over time: 66% (SD 16) at 48 weeks, 60% (SD 16) at 96 weeks, 52% (SD 18) at 144 weeks. The most common reason for treatment cessation was participant decision (11%, SD 6.6). Efficacy was higher with ‘Preferred’ than ‘Alternative’ regimens (as defined by 2013 United States antiretroviral guidelines): 75% vs. 65%, respectively, difference 10%; 95%CI 7.6 to 15.4; p,0.001. In 98 groups (45%) reporting efficacy stratified by pre-treatment viral load (, or $100,000 copies/mL), efficacy was greater for the lower stratum (70% vs. 62%, respectively, difference 8.4%; 95%CI 6.0 to 10.9; p, 0.001). This difference persisted within ‘Preferred’ regimens. Greatest efficacy was associated with use of tenofoviremtricitabine (vs. other nucleoside analogue backbones) and integrase strand transfer inhibitors (vs. other third drug classes). Conclusion: Initial antiretroviral treatments for HIV-1 to date appear to have suboptimal long-term efficacy, but are more effective when commenced at plasma viral loads ,100,000 copies/mL. Rising viral load should be considered an indication for starting treatment. Integrase inhibitors offer a treatment advantage (vs. other third drug classes). Citation: Lee FJ, Amin J, Carr A (2014) Efficacy of Initial Antiretroviral Therapy for HIV-1 Infection in Adults: A Systematic Review and Meta-Analysis of 114 Studies with up to 144 Weeks’ Follow-Up. PLoS ONE 9(5): e97482. doi:10.1371/journal.pone.0097482 Editor: Mark Wainberg, McGill University AIDS Centre, Canada Received January 23, 2014; Accepted April 20, 2014; Published May 15, 2014 Copyright: 2014 Lee et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: Frederick J. Lee currently receives research funding from the National Health and Medical Research Council of Australia (Grant Number 1017991), which supported this work. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have read the journals’ policy and have the following conflicts to declare: Frederick J. Lee currently receives research funding from the National Health and Medical Research Council of Australia (Grant Number 1017991), and has received a Clinical Immunology Society Fellowship, travel sponsorships from the Australasian Society of Clinical Immunology and Allergy and the Australasian Society for HIV Medicine, conference sponsorship from MSD and Gilead Sciences, and consultancy fees from MSD and AstraZeneca. Andrew Carr has received research funding from Baxter, Gilead Sciences, MSD, Pfizer and ViiV Healthcare, consultancy fees from Gilead Sciences, MSD, and ViiV Healthcare, lecture and travel sponsorships from Bristol-Myers Squibb, Gilead Sciences, Janssen, MSD, and ViiV Healthcare, and has served on advisory boards for Gilead Sciences, MSD, and ViiV Healthcare. Janaki Ami declares no conflict of interest. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. * E-mail:
[email protected] resource-rich settings. As of February 2013, an NRTI backbone of tenofovir-emtricitabine/lamivudine, with either efavirenz (NNRTI), raltegravir (INSTI), or ritonavir-boosted atazanavir or darunavir (PI) comprises ‘Preferred’ initial therapy [1]. When to commence cART remains guided by the clinical stage and CD4 lymphocyte count; pre-treatment plasma HIV viral load was removed as an indication for starting cART in 2007. While such recommendations arise from serial evaluation of individual studies by expert bodies, a systematic review of outcomes across multiple
Introduction Combination antiretroviral therapy (cART) for human immunodeficiency virus (HIV)-1 infection typically comprises a ‘backbone’ – two nucleoside analogue reverse transcriptase inhibitors (NRTI) – and a third drug – either a non-nucleoside reverse transcriptase inhibitor (NNRTI), a protease inhibitor (PI) or an integrase strand-transfer inhibitor (INSTI). The United States Department of Health and Human Services (DHHS) guidelines form a major basis for HIV public policy in PLOS ONE | www.plosone.org
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drug. No language restriction was applied. Abstracts from the following key scientific meetings between 2008 and 2012 were also searched: Conference on Retroviruses and Opportunistic Infections; International AIDS Society; Interscience Conference on Antimicrobial Agents and Chemotherapy; and the International Congress on Drug Therapy in HIV. Product labels and medical reviews of antiretroviral drugs published by the United States Food and Drug Administration and the European Medicines Agency between January 1, 1996 and December 31, 2012 were reviewed manually. The above data were combined with all eligible 1995–2008 studies included in our earlier systematic review [4]. All were manually reviewed in duplicate by an author (FJL) for eligibility before being combined with results of the latest search. Discrepancies were discussed with a second author (AC). Study synopses accessible on the following pharmaceutical company websites up to December 31, 2012 were reviewed: Abbott; Boehringer-Ingelheim; Bristol-Myers Squibb; Gilead Sciences; GlaxoSmithKline; and Roche. Other antiretroviral manufacturers did not have such websites. All manufacturers were approached for relevant data missing from the above sources; data were provided by Bristol-Myers Squibb (August 15, 2012), Gilead Sciences (August 10, 2012), MSD (July 31, 2012), and ViiV Healthcare (November 29, 2012). The PRISMA flow diagram is depicted in Figure 1.
studies may reveal characteristics associated with success/failure, and so inform drug development, future study design, treatment guidelines and ultimately patient care. Of the available meta-analyses of initial cART, most focus upon specific comparisons, or earlier studies [2,3,4,5,6]. None has evaluated outcomes beyond 48 weeks or by the regimen type (‘Preferred’ vs. ‘Alternative’). Much data (some of it unpublished) have been generated since the last broad-ranging analysis of initial cART efficacy [4], and an updated comprehensive assessment of initial cART efficacy and its associations is warranted.
Methods This systematic review evaluated all prospective studies of initial cART in adults reported through December 31, 2012. The primary outcome measure was antiviral efficacy, defined as undetectable (study-defined) plasma HIV viral load reported on an intention-to-treat (study-defined) basis. Substitution of any initial drug was regarded as treatment failure. Secondary outcomes were efficacy at weeks 48, 96 and 144, change in efficacy postweek 48, and premature cessation of initial cART. Subgroup analyses of efficacy were performed within pre-treatment HIV viral load strata ($ or ,100,000 copies/mL plasma) and by use of a ‘Preferred’ or ‘Alternative’ regimen as per the February 2013 edition of the DHHS guidelines. Additionally, we aimed to identify characteristics associated with heterogeneity of summarised efficacy, and premature treatment cessation due to participant decision, adverse events or virological failure.
Data extraction
Conduct of this study was in accordance with the PRISMA Statement [7]. The protocol/analysis plan are available from the editors or authors upon request (Protocol S1). This review aimed to include all studies of initial cART, subject to strict eligibility criteria to ensure data quality. Included studies: were conducted in consenting, treatment-naı¨ve, HIV-1-infected adults; were a prospective cohort or randomised trial; reported efficacy data; and had a minimum of 48 weeks’ follow-up. Comparative trials of initial cART were assessed only for the duration that the original randomisation was preserved. We excluded studies of: retrospective or cross-sectional design; cART regimens categorised as not to be offered at any time in key treatment guidelines from 1996 through 2013 [1,8,9]; multiple/ variable regimens within a single study arm; and directly-observed therapy. However, treatment groups with fixed, unspecified, dualNRTI backbones and a common third drug were permitted [10]. Studies of novel, class-sparing regimens were considered for inclusion on an individual basis after review of the efficacy data (relative to standard triple-drug therapy based on a dual-NRTI backbone) by at least two authors. Boosting-dose ritonavir was not regarded an antiretroviral drug (but included in pill counts/dosing requirements). Apart from excluding any study presenting ,48 weeks of efficacy data, eligibility criteria were the same as our previous systematic review of pre-2008 studies [4].
Study arms were regarded as individual groups. For every group, characteristics collected were: year of commencement; participant numbers; latest reporting format; study characteristics; eligibility criteria; participant/disease characteristics; treatment characteristics; efficacy; and rates of premature treatment cessation. Efficacy data were collected cumulatively through study weeks 48, 96 and 144. Dosing requirements were assumed to be in accordance with the product label at the time of study initiation or subsequent approval, unless otherwise stated. Efficacy data reported using the ‘time to loss of virological response’ (TLOVR) algorithm were collected preferentially. The newer ‘Snapshot’ algorithm was not considered, due to the small numbers and reported similarity of results to TLOVR [11]. All plasma viral load assays were considered equivalent – when more than one threshold was reported (e.g. both ,400 and ,50 copies/mL plasma), the lowest was used. Data were abstracted manually in triplicate by one author (FJL) using a standardised data collection form. Ethnicity was recorded as ‘white’, ‘black’ or ‘other’, defined as previously [4]. Cause of premature treatment cessation was categorised as one of: adverse event, participant decision (withdrawal, loss to follow-up), virological failure, and other. No imputation was made for missing data (marked as unavailable). Completed forms and the resulting electronic database were audited by a second author (AC) to ensure data quality.
Data sources and search strategy
Summary measures and data synthesis
The search period was January 1, 2008 to December 31, 2012. Electronic databases searched were: MEDLINE; Cochrane Central Register of Controlled Trials; United States National Institutes of Health clinical trials registry; and the International Standard Randomised Controlled Trial Numbers registry. For each of these, the search strategy was: ‘(‘‘drug’’) AND (HIV OR antiretroviral) AND (cohort OR randomised trial)’, where ‘‘drug’’ was the generic or pre-approval code name of an antiretroviral
The study arm was the unit of analysis, with clustering by trial accounted for. Principal summary measures were the proportion of participants per group and the mean (for continuous variables, including proportions). For summary measures across study arms, outcomes were expressed as a mean percentage, weighted for group size. Standard error of the mean was used to adjust for group size differences and heterogeneity was quantified using the I2 statistic [12]. Adjusted absolute difference in outcomes within
Study protocol and eligibility criteria
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Figure 1. PRISMA statement 2009 flow chart. Diagram depicts each step of the study selection process undertaken in this systematic review and meta-analysis, including the reasons for exclusion. doi:10.1371/journal.pone.0097482.g001
categorical variables was expressed as a percentage coefficient. Differences between means were compared using Student’s t-test. A multivariable, random-effects, linear regression approach was used to explore sources of heterogeneity in measures of efficacy and treatment cessation. Variables pre-selected for testing included all study, participant, disease and treatment characteristics as well as adverse events (Table 1). Year of study commencement was excluded from the primary predictor analysis, because of its likely relationship to other variables (drug potency/tolerability, pill/dose counts, availability of genotypic testing, emphasis on maximum pill adherence). Fixed NRTI backbones and third drug classes were compared, not individual drugs. This was done to retain analytical power and sensitivity across subgroup analyses. Furthermore, comparative prospective trials are powered for non-inferiority, and no clear superiority has hitherto been demonstrated for individual third drugs within a given class for initial cART – excepting boosted vs. unboosted PI. Unboosted PIs, (including unboosted atazanavir) were considered here as a separate class. Backwards, stepwise selection was used: only those statistically significant (p#0.05) in univariable analysis were further assessed in multivariable models. Additionally, where covariate data were missing for .20% of individuals within a group, that group was excluded for assessment of that covariate. For stratified antiviral efficacy (pre-treatment viral load; regimen type) tests for interaction were conducted first. Only variables with statistically significant interactions with the stratified PLOS ONE | www.plosone.org
outcome were further interrogated using the aforementioned linear regression approach. The following pre-planned sensitivity analyses were performed for the predictor linear regression models: (1) including only recent data by excluding studies commenced prior to 2005; (2) excluding cohorts and abstracts to correct for potential differences in study/ report quality; and (3) including year of commencement as a variable. Four post-hoc analyses were performed: (1) descriptive efficacy/premature cessation data for groups using tenofoviremtricitabine and abacavir-lamivudine, both overall and stratified by pre-treatment plasma viral load $ and ,100,000 copies/mL; (2) a variant of the primary linear regression model for overall efficacy conducted after exclusion of all abacavir-lamivudine groups without HLA-B*5701 screening; (3) descriptive efficacy/ premature cessation data and a linear regression model for overall efficacy when considering raltegravir separately from other INSTI drugs; and (4) descriptive efficacy/premature cessation data and a linear regression model for overall efficacy when considering unboosted atazanavir separately from other unboosted PIs (Tables S1-S6 in Appendix S1: Supplementary post-hoc stratified analyses). All analyses were performed using STATA, Version 11 (StataCorp LP, College Station, Texas, USA). The meta suite of commands were used for combining data across trial arms.
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Table 1. Characteristics included in linear regression analyses.
Participant pre-treatment characteristics
Treatment characteristics
Safety outcomes
study phase
risk factors for HIV infection
daily pill count
serious adverse events
placebo-controlled
sex
daily dose count
grade 2 to 4 clinical adverse events*
geography of recruitment
prior CDC category C events
dosing requirements relative to food
grade 3 to 4 laboratory adverse events*
intention-to-treat strategy
race
NRTI backbone
study eligibility criteria (haemoglobin, liver function, CD4 count, HIV-1 plasma viral load, pre-treatment genotyping)
pre-treatment HIV-1 plasma viral load
third drug class
Study design
pre-treatment CD4 count hepatitis B or C co-infection *Moderate or greater severity – graded according to 2009 Division of AIDS Classification. CDC, Centers for Disease Control and Prevention; HIV, human immunodeficiency virus; NRTI, nucleoside reverse transcriptase inhibitor. doi:10.1371/journal.pone.0097482.t001
to either an NNRTI (coefficient 11.9%; 95%CI 4.6 to 19.2; p = 0.002) or a boosted PI (coefficient 12.7%; 95%CI 4.6 to 19.2; p = 0.001). There was no difference in efficacy of NNRTI compared to boosted PI regimens.
Results Study selection The search (2008-2012) yielded 2,272 studies (2,008 publications, 306 abstracts), with 42 duplicates; 45 studies met the eligibility criteria. Following review and addition of pre-2008 studies (removal of duplicates and one pre-2008 cohort with only 24 weeks of follow-up), 114 studies (103 publications, 11 abstracts) were included (Table 2).
Change in efficacy by follow-up duration Efficacy at 96 and 144 weeks was reported for 85 (39%, 19,959 participants) and 25 (12%, 8,330 participants) groups, respectively. For these groups, mean efficacy at 48 weeks was 66% (SD 16), declining to 60% (SD 16) at week 96, then 52% (SD 18) at week 144; the mean fall in efficacy was 8.3% (SD 3.9) between weeks 48 and 96, with a further 6.3% (SD 5.3) decline between weeks 96 and 144. Type of NRTI backbone (favouring tenofovir-emtricitabine, p, 0.001), third drug class (favouring INSTI, p,0.001) and efficacy analysis method (favouring ‘missing equals failure’, p = 0.001), eligibility by pre-treatment resistance genotype (favouring restriction, p = 0.007) and CD4 lymphocyte count (favouring no restriction, p = 0.001) were each associated on multivariable analysis with greater efficacy at week 48. At week 96, greater efficacy was associated with: phase 2 studies (vs. phase 3 or 4, p, 0.001) and no eligibility restriction for CD4 count (p,0.001). The r2 values were 66% and 39% for weeks 48 and 96, respectively. Due to insufficient data, a stable multivariable model could not be generated for efficacy through week 144. For those studies reporting efficacy data through to week 96, a multivariable analysis for the decline in efficacy between weeks 48 and 96 was performed. Lesser decline was associated with phase 2 studies (p = 0.002), placebo use (p,0.001), and NRTI backbone (p = 0.002), but there was no significant difference between use of tenofovir-emtricitabine and abacavir-lamivudine.
Study and participant characteristics Of 114 included studies, 97 (85%) were randomised trials and 17 (15%) prospective cohorts (Table 3), encompassing 216 treatment groups with 40,124 participants (median 112 participants/group; interquartile range 63 to 200). This represents 73 new groups (32 randomised trials, 3 cohorts, 17,057 participants) since our earlier review [4]. Participant and treatment characteristics are shown in Table 4; these were similar in terms of NRTI backbone and third drug class, demographics and disease stage for each analysis population (data not shown).
Overall efficacy: all studies Mean overall efficacy was 60% (SD 16) after a mean follow-up of 82 weeks (SD 38) with greater efficacy in more recent studies (Table 5, Figure 2). Collected data were highly heterogeneous (I2 = 96%). Study phase, intention-to-treat analysis method, genotype/CD4 eligibility restrictions, NRTI backbone, third drug class, daily pill and dose counts were identified as the primary sources of efficacy heterogeneity on univariable analysis (Table 6). In the multivariable analysis, higher efficacy was associated with: NRTI backbone (favouring tenofovir-emtricitabine, p,0.001); third drug class (favouring INSTI, p,0.001); and studies using intention-to-treat algorithm (favouring ‘missing equals failure’, p,0.001). This model accounted for 56% of the variance (r2) in reported efficacy. Tenofovir-emtricitabine was associated with higher efficacy than abacavir-lamivudine (coefficient 7.6%; 95% confidence interval [CI] 2.6 to 12.7; p = 0.003). Only two groups, both 96 weeks in duration, used pre-treatment HLA-B*5701 screening before administering abacavir; overall weighted efficacy of these abacavir groups was 55% (SD 13), vs. 63% (SD 6.4) for the 24 groups without HLA-B*5701 screening. Amongst third drug classes, INSTI use was associated with higher efficacy compared PLOS ONE | www.plosone.org
Efficacy stratified by regimen type: February 2013 DHHS guidelines A total of 63 groups (29%, 14,233 participants) were allocated either a 2013 DHHS ‘Preferred’ regimen (27 groups, 13%, 5,677 participants) or an ‘Alternative’ regimen (39 groups, 18%, 9,305 participants). Amongst the ‘Preferred’ regimens, 14 groups (2,729 participants) initiated efavirenz: efficacy 72% (SD 7.6) over 108 weeks (SD 40); nine groups (1,776 participants) initiated atazanavir/ritonavir: efficacy 75% (SD 7.3) over 87 weeks (SD 42); one group (343 participants) initiated darunavir/ritonavir:
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Table 2. Included studies and treatment groups.
Study title (Name) [Reference]
Study type
Placebo control
1100.1486 (VERxVE) [Antivir Ther 2011; 16: 759–69]
RCT
Yes
Included treatment groups [n = ] TDF-FTC+NVP 400 mg once-daily [505] vs. TDF-FTC+NVP 200 mg twice-daily [508]
Duration (weeks) 48
2NN [Lancet 2004; 363: 1253–63.]
RCT
No
d4T-3TC+EFV [381] vs. d4T-3TC+NVP [378]
48
A4001026 (MERIT) [J Infect Dis 2010; 201: 803–13]
RCT
Yes
AZT-3TC+EFV [311] vs. AZT-3TC+MVC [303]
96
A4001078 [6th IAS Conference 2011 (abstract TUAB0103)]
RCT
No
rAZV+MVC [60] vs. TDF-FTC+rAZV [61]
96
A5271015 [6th IAS Conference 2011 (abstract TUAB0101)]
RCT
Yes
TDF-FTC+LRV [130] vs. TDF-FTC+EFV [63]
48
ABCDE [J Acquir Immun Defic Syndr 2007; 44: 139–47]
RCT
No
ABC-3TC+EFV [115] vs. d4T-3TC+EFV [122]
96
ACH443-015 [CROI 2010 (abstract K-131)]
RCT
Yes
TDF-ELV+EFV [39] vs. TDF-3TC+EFV [37]
96
ACTG 384 [N Engl J Med 2003; 349: 2293–303]
RCT
Yes
AZT-3TC+EFV [155] vs. AZT-3TC+NFV [155] vs. d4T-ddI+EFV [155] vs. d4T-ddI+NFV [155]
96
ACTG 5095 [N Engl J Med 2004; 350: 1850–61]
RCT
Yes
AZT-3TC+EFV [382]
144
ACTG A5073 [Clin Infect Dis 2010; 50: 1041–52]
RCT
No
2 NRTIs+rLPV twice-daily [159] vs. 2 NRTIs+rLPV once-daily [161]
48
ACTG A5142 [N Engl J Med 2008; 358: 2095–106]
RCT
No
2 NRTIs+EFV [250] vs. 2 NRTIs+rLPV [253] vs. rLPV+EFV [250]
96
ACTG A5175 (PEARLS) [PLoS Med 2012; 9: e1001290]
RCT
No
TDF-FTC+EFV [526] vs. AZT-3TC+EFV [519]
96
ACTG A5202 [Ann Intern Med 2011; 154: 445–56]
RCT
Yes
ABC-3TC+EFV [465] vs. ABC-3TC+rAZV [463] vs. TDF-FTC+EFV [464] vs. TDF-FTC+rAZV [465]
144
ACTG A5208 (OCTANE 2) [PLoS Med 2012; 9: e1001236]
RCT
No
TDF-FTC+NVP [249] vs. TDF-FTC+rLPV [251]
144
ACTG A5262 [AIDS 2011; 25: 2113–22]
Cohort
No
rDRV+RAL [112]
48
Advanced HIV Mexico [J Acquir Immun Defic Syndr 2010; 53: 582–8]
RCT
No
AZT-3TC+EFV [95] vs. AZT-3TC+rLPV [94]
48 144
Advanz [AIDS Res Hum Retroviruses 2010; 26: 747–57]
RCT
No
AZT-3TC+EFV [34] vs. AZT-3TC+rIDV [31]
AI424-007 [J Acquir Immun Defic Syndr 2003; 32: 18–29]
RCT
Yes
d4T-ddI+AZV [103] vs. d4T-ddI+NFV [103]
48
AI424-008 [AIDS 2003; 17: 2603–14]
RCT
Yes
d4T-3TC+AZV [181] vs. d4T-3TC+NFV [91]
48
AI424-034 [J Acquir Immun Defic Syndr 2004; 36: 1001–19]
RCT
Yes
AZT-3TC+AZV [404] vs. AZT-3TC+EFV [401]
48
AI424-089 [J Acquir Immun Defic Syndr 2008; 47: 161–7]
RCT
No
d4T-3TC+rAZV [95] vs. d4T-3TC+AZV [104]
96
AI424-138 (CASTLE) [Lancet 2008; 372: 646–55]
RCT
No
TDF-FTC+rAZV [438] vs. TDF-FTC+rLPV [440]
96
AI454-148 [FDA Product Label (Videx): http://www.accessdata.fda.gov/drugsatfda_docs/ label/2006/020154s50,20155s39,20156s40,21183s16lbl.pdf]
RCT
No
AZT-3TC+NFV [253] vs. d4T-ddI+NFV [503]
48
AI454-152 [J Acquir Immun Defic Syndr 2002; 31: 399–403]
RCT
No
AZT-3TC+NFV [253] vs. d4T-ddI+NFV [258]
48
AI455-096 [43rd Annual ICAAC 2003 (abstract H843)]
RCT
Yes
d4T-3TC once-daily+EFV [74] vs. d4T-3TC twice-daily+EFV [76]
96
AI455-099 [43rd Annual ICAAC 2003 (abstract H843)]
RCT
Yes
d4T-3TC once-daily+EFV [392] vs. d4T-3TC twice-daily+EFV [391]
96
Altair [Clin Infect Dis 2010; 51: 855–64]
RCT
No
TDF-FTC+EFV [114] vs. TDF-FTC+rAZV [105] vs. TDF-FTC+ABC-3TC [103]
48
ANRS 12-04 (IMEA 011) [AIDS 2003; 17: 1017–22]
Cohort
No
ddI-3TC+EFV [40]
48
ANRS 129 (BKVIR) [50th Annual ICAAC 2010 (abstract H232)]
Cohort
No
TDF-FTC+EFV [69]
48
APV109141 [HIV Clin Trials 2009; 10: 356–67]
RCT
No
ABC-3TC+rFPV once-daily [106] vs. ABC-3TC+rFPV twice-daily [106]
48
APV30001 (NEAT) [J Acquir Immun Defic Syndr 2004; 35: 22-32]
RCT
No
ABC-3TC+FPV [166] vs. ABC-3TC+NFV [83]
48
APV30002 (SOLO) [AIDS 2004; 18: 1529–37]
RCT
No
ABC-3TC+NFV [327] vs. ABC-3TC+rFPV [322]
48
ARES [HIV Clin Trials 2005; 5: 235–45]
RCT
No
d4T-ddI+NFV [26] vs. ddI-3TC+NVP [22] vs. ddI-3TC+rSQV [23]
48
ARTEN [Antivir Ther 2011; 16: 339–48]
RCT
No
TDF-FTC+NVP [376] vs. TDF-FTC+rAZV [193]
48
AVANTI 2 [AIDS 2000; 14: 367–74]
RCT
Yes
AZT-3TC+IDV [52]
48 48
BI1046 (INCAS) [JAMA 1998; 279: 930–7]
RCT
Yes
AZT-ddI+NVP [51]
BI1129 (ATLANTIC) [AIDS 2003; 17: 987–99]
RCT
No
d4T-ddI+3TC [104] vs. d4T-ddI+IDV [94] vs. d4T-ddI+NVP [85]
96
BI1182.33 [8th International Congress on Drug Therapy 2006 (abstract PL13.4)]
RCT
No
TDF-3TC+rLPV [185] vs. TDF-3TC+r(100 mg)TPV [187] vs. TDF-3TC+r(200 mg)TPV [186]
48
BMS-001 (START 1) [AIDS 2000; 14: 1591–600]
RCT
Yes
AZT-3TC+IDV [103] vs. d4T-3TC+IDV [101]
48
BMS-002 (START 2) [AIDS 2000; 14: 1601–10]
RCT
Yes
AZT-3TC+IDV [103] vs. d4T-ddI+IDV [102]
48
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Table 2. Cont.
Study title (Name) [Reference]
Study type
Placebo control
Included treatment groups [n = ]
Duration (weeks)
CCTG589 [6th IAS Conference 2011 (abstract CDB336)]
RCT
No
rLPV+RAL [26] vs. TDF-FTC+EFV [25]
48
Chelsea Westminster [Antivir Ther 2006; 11: 73–8]
RCT
No
AZT-3TC+EFV [56]
48
CNA109586 (ASSERT) [J Acquir Immun Defic Syndr 2010; 55: 49–57]
RCT
No
ABC-3TC+EFV [192] vs. TDF-FTC+EFV [193]
96
CNA30021 (ZODIAC) [J Acquir Immun Defic Syndr 2005; 38: 417–25]
RCT
Yes
ABC-3TC once-daily+EFV [384] vs. ABC twice-daily-3TC+EFV [386]
48
CNA30024 [Clin Infect Dis 2004; 39: 1038–46]
RCT
Yes
ABC-3TC+EFV [324] vs. AZT-3TC+EFV [325]
48
CNAAB3005 [JAMA 2001; 285: 1155–63]
RCT
Yes
AZT-3TC+ABC [262] vs. AZT-3TC+IDV [264]
96
CNAB3014 [Curr Med Res Opin 2004; 20: 1103–14]
RCT
No
AZT-3TC+ABC [169] vs. AZT-3TC+IDV [173]
48
CNAF3007 [Antivir Ther 2003; 8: 163–71]
RCT
No
AZT-3TC+ABC [98] vs. AZT-3TC+NFV [97]
48
COL100758 [AIDS Res Hum Retroviruses 2009; 25: 395–403]
RCT
No
ABC-3TC+r(100 mg)FPV [58] vs. ABC-3TC+r(200 mg)FPV [57]
96
COL102060 (SHARE) [HIV Clin Trials 2008; 9: 152–63]
Cohort
No
ABC-3TC+rAZV [111]
48
COL103952 (ALERT) [Smith KY, et al. AIDS Res Ther 2008; 5: 5]
RCT
No
TDF-FTC+rAZV [53] vs. TDF-FTC+rFPV [53]
48
COL111429 (SHIELD) [HIV Clin Trials 2010; 11: 260–9]
Cohort
No
ABC-3TC+RAL [35]
96
COMBINE [Antivir Ther 2002; 7: 81–90]
RCT
No
AZT-3TC+NFV [70] vs. AZT-3TC+NVP [72]
48
CTN 177 [J Acquir Immun Defic Syndr 2009; 50: 335–7]
RCT
No
AZT-3TC+NVP [26] vs. AZT-3TC+rLPV [25]
96
Danish [AIDS 2003; 17: 2045–52]
RCT
No
AZT-3TC+rSQV [60] vs. d4T-ddI+ABC [60]
48
DART [AIDS 2006; 20: 1391–9]
Cohort
No
AZT-3TC+TDF [300]
48
DMP 266-006 [N Engl J Med 1999; 341: 1865–73]
RCT
No
AZT-3TC+EFV [422] vs. AZT-3TC+IDV [415] vs. EFV+IDV [429]
144
EARTH 2 [J Acquir Immun Defic Syndr 2000; 25: 26–35]
RCT
No
d4T-3TC+IDV [32]
48
EPV20001 [Clin Infect Dis 2004; 39: 411–8]
RCT
Yes
AZT-3TC once-daily+EFV [278] vs. AZT-3TC twice-daily+EFV [276]
48 96
EPZ104057 (HEAT) [AIDS 2009; 23: 1547–56]
RCT
Yes
ABC-3TC+rLPV [343] vs. TDF-FTC+rLPV [345]
ESS100327 (ACTION) [AIDS Res Ther 2009; 6: 3]
RCT
No
AZT-3TC+ABC [139] vs. AZT-3TC+AZV [140]
48
ESS100732 (KLEAN) [Lancet 2006; 368: 476–82]
RCT
No
ABC-3TC+rFPV [443] vs. ABC-3TC+rLPV [444]
48
ESS30009 [J Infect Dis 2005; 192: 1921–30]
RCT
No
ABC-3TC+EFV [169]
48
ESS40001 (CLASS) [J Acquir Immun Defic Syndr 2006; 43: 284–92]
RCT
No
ABC-3TC+d4T [98] vs. ABC-3TC+EFV [97] vs. ABC-3TC+rFPV [96]
96
ESS40002 [HIV Med 2006; 7: 85–98]
RCT
No
AZT-3TC+ABC [85] vs. AZT-3TC+NFV [88] vs. d4T-3TC+NFV [81]
96
FOCUS [J Internat AIDS Soc 2006; 8: 36]
RCT
No
2 NRTIs+EFV [83] vs. 2 NRTIs+rSQV [82]
48
FTC301A [JAMA 2004; 292: 180–90]
RCT
Yes
d4T-ddI+EFV [285] vs. d4T-FTC+EFV [286]
48
GEMINI [J Acquir Immun Defic Syndr 2009; 50: 367–74]
RCT
No
TDF-FTC+rSQV [167] vs. TDF-FTC+rLPV [170]
48
GESIDA 3093 [Clin Infect Dis 2008; 47: 1083–92]
RCT
No
ddI-3TC+EFV [186] vs. AZT-3TC+EFV [183]
48 144
GS-01-934 [J Acquir Immun Defic Syndr 2006; 43: 535–40]
RCT
No
TDF-FTC+EFV [244] vs. AZT-3TC+EFV [243]
GS-99-903 [JAMA 2004; 292: 191–201]
RCT
Yes
TDF-3TC+EFV [299] vs. d4T-3TC+EFV [301]
144
GS-US-216-0105 [AIDS 2011; 25: 1881–6]
RCT
Yes
TDF-FTC+cobicistat-AZV [50] vs. TDF-FTC+rAZV [29]
48
GS-US-236-0102 [Lancet 2012; 379: 2439–48]
RCT
Yes
TDF-FTC+cobicistat-EVG [348] vs. TDF-FTC+EFV [352]
48
GS-US-236-0103 [Lancet 2012; 379: 2429–38]
RCT
Yes
TDF-FTC+cobicistat-EVG [353] vs. TDF-FTC+rAZV [355]
48 48
GS-US-236-0104 [AIDS 2011; 25: F7–12]
RCT
Yes
TDF-FTC+cobicistat-EVG [48] vs. TDF-FTC+EFV [23]
GS-US-264-0110 [J Int AIDS Soc 2012; 15(S4): 18221]
RCT
No
TDF-FTC+EFV [392] vs. TDF-FTC+RPV [394]
48
HIV-NAT 003 [J Acquir Immun Defic Syndr 2001; 27: 116–23]
RCT
No
AZT-3TC+ddI [53]
48
ING112276 (SPRING-1) [Lancet Infect Dis 2012; 12: 111–8]
RCT
No
2 NRTIs+DTG [155] vs. 2 NRTIs+EFV [50]
96
LAKE [Antiviral Res 2010; 85: 403–8]
RCT
No
ABC-3TC+EFV [63] vs. ABC-3TC+rLPV [63]
48
LORAN [Open AIDS J 2011; 5: 44–50]
RCT
No
AZT-3TC+rLPV [35]
48
LOREDA [6th IAS Conference 2011 (abstract CDB354)]
Cohort
No
3TC+rLPV [39]
48
M02-418 [J Acquir Immun Defic Syndr 2006; 43: 153–60]
RCT
No
TDF-FTC+rLPV once-daily [115] vs. TDF-FTC+rLPV twice-daily [75]
96
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Efficacy of Initial Antiretroviral Therapy: A Systematic Review
Table 2. Cont.
Study title (Name) [Reference]
Study type
Placebo control
M05-730 [J Acquir Immun Defic Syndr 2009; 50: 474–81]
RCT
No
TDF-FTC+rLPV once-daily [333] vs. TDF-FTC+rLPV twice-daily [331]
M10-336 (PROGRESS) [HIV Clin Trials 2011; 12: 255–67]
RCT
No
TDF-FTC+rLPV [105] vs. rLPV+RAL [101]
96
M97-720 [AIDS 2001; 15: 1–9]
Cohort
No
d4T-3TC+rLPV [100]
96
M98-863 [N Engl J Med 2002; 346: 2039–46]
RCT
Yes
d4T-3TC+NFV [327] vs. d4T-3TC+rLPV [326]
48
M99-056 [J Infect Dis 2004; 189: 265–72]
RCT
No
d4T-3TC+rLPV once-daily [19] vs. d4T-3TC+rLPV twice-daily [19]
48 144
Included treatment groups [n = ]
Duration (weeks) 96
MK0518-004 [J Acquir Immun Defic Syndr 2007; 46: 125–33]
RCT
Yes
TDF-3TC+RAL [160] vs. TDF-3TC+EFV [38]
MK0518-021 (STARTMRK) [Lancet 2009; 374: 796–806]
RCT
Yes
TDF-FTC+RAL [281] vs. TDF-FTC+EFV [282]
144
MK0518-071 (QDMRK) [Lancet Infect Dis 2011; 11: 907–15]
RCT
Yes
TDF-FTC+RAL once-daily [382] vs. TDF-FTC+RAL twice-daily [388]
48
MONARK [AIDS 2008; 22: 385–93]
RCT
No
AZT-3TC+rLPV [53]
48
N2R [Clin Infect Dis 2009; 48: 1752–9]
RCT
No
d4T-3TC+EFV [71] vs. d4T-3TC+NVP [71]
48
NEWART [Int J Clin Pract 2011; 65: 1240–9]
RCT
No
TDF-FTC+NVP [75] vs. TDF-FTC+rAZV [77]
48
Nigerian ARV Program Cohort [J Acquir Immun Defic Syndr 2005; 40: 65–9]
Cohort
No
d4T-3TC+NVP [50]
48
NORA (DART substudy) [HIV Med 2010; 11: 334–44]
RCT
Yes
AZT-3TC+ABC [300] vs. AZT-3TC+NVP [300]
48
NVP China Cohort [PLoS One 2008; 3: e3918]
Cohort
No
AZT-ddI+NVP [65] vs. d4T-3TC+NVP [69] vs. AZT-3TC+NVP [64]
48 48
ONCE Cohort [Antivir Ther 2002; 6: 249–53]
Cohort
No
ddI-3TC+EFV [75]
ONCE RCT [Antivir Ther 2003; 8: 339–46]
RCT
No
AZT-3TC+EFV [34] vs. AZT-3TC+NFV [34]
48
OZCOMBO 1 [AIDS 2000; 14: 1171–80]
RCT
No
AZT-3TC+IDV [35] vs. d4T-3TC+IDV [34] vs. d4T-ddI+IDV [37]
48 48
OZCOMBO 2 [HIV Clin Trials 2002; 3: 177–85]
RCT
No
AZT-3TC+NVP [20] vs. d4T-3TC+NVP [22] vs. d4T-ddI+NVP [23]
PROAB3001 [Antivir Ther 2000; 5: 215–25]
RCT
Yes
AZT-3TC+APV [116]
48
QUAD [J Antimicrob Chemother 2005; 55: 246–51]
RCT
No
AZT-3TC+EFV [26]
48
SCAN [AIDS 2000; 14: 2485–94]
RCT
No
d4T-ddI+NVP once-daily [45] vs. d4T-ddI+NVP twice-daily [44]
48
SENC [HIV Clin Trials 2002; 3: 186–94]
RCT
No
d4T-ddI+EFV [31] vs. d4T-ddI+NVP [36]
48
South African Workplace HIV Program [17th AIDS Conference 2008 (abstract MOPE0046)]
Cohort
No
AZT-3TC+EFV [1416]
144
Spanish ddI-3TC-NVP QD [Antivir Ther 2005; 10: 605–14]
Cohort
No
ddI-3TC+NVP [70]
48
Swiss Ritonavir [J Acquir Immun Defic Syndr 2000; 23: 17–25]
RCT
No
AZT-3TC+r(1200 mg) [23]
96
Thai Indinavir [Antivir Ther 2005; 10: 911–6]
Cohort
No
d4T-3TC+rIDV [80]
96
TMC114-C211 (ARTEMIS) [AIDS 2008; 22: 1389–97]
RCT
No
TDF-FTC+rDRV [343] vs. TDF-FTC+rLPV [346]
96
TMC125 VIR2038 (SENSE) [AIDS 2011; 25: 2249–58]
RCT
Yes
2 NRTIs+ETR [79] vs. 2 NRTIs+EFV [78]
48
TMC278-C204 [AIDS 2010; 24: 55–65]
RCT
No
2 NRTIs+RPV [279] vs. 2 NRTIs+EFV [89]
144
TMC278-TiDP6-C209/215 (ECHO/THRIVE) [J Acquir Immun Defic Syndr 2012; 60: 33–42]
RCT
Yes
2 NRTIs+RPV [686] vs. 2 NRITs+EFV [682]
96
VACH [J Antimicrob Chemother 2009; 63: 189–96]
Cohort
No
AZT-3TC+EFV [409] vs. ddI-3TC+EFV [219]
144
VESD [HIV Clin Trials 2005; 6; 320–8]
Cohort
No
ddI-3TC+EFV [167]
48
VIRGO [Antivir Ther 2000; 5: 267–72]
Cohort
No
d4T-ddI+NVP [100]
48
ABC, abacavir; AZT, zidovudine; APV, amprenavir; AZV, atazanavir; d4T, stavudine; ddI, didanosine; DRV, darunavir; DTG, dolutegravir; EFV, efavirenz; ELV, elvucitabine; ETR, etravirine; EVG, elvitegravir; FPV, fosamprenavir; FTC, emtricitabine; IDV, indinavir; LPV, lopinavir; MVC, maraviroc; NFV, nelfinavir; NVP, nevirapine; r, ritonavir; RPV, rilpivirine; RAL, raltegravir; SQV, saquinavir; TDF, tenofovir; TPV, tipranavir. doi:10.1371/journal.pone.0097482.t002
efficacy 79% over 96 weeks; and three groups (829 participants) initiated raltegravir: efficacy 82% (SD 7.9) over 99 weeks (SD 59). ‘Preferred’ regimen efficacy was 75% (SD 7.9) over a mean follow up of 99 weeks (SD 41), compared to 66% (SD 6.6) over 82 weeks (SD 35) with ‘Alternative’ regimens (difference 10%; 95%CI 7.6 to 15.4; p,0.001). This difference persisted when stratified by high (difference 10%; 95%CI 3.0 to 17.0; p = 0.006) and low
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(difference 16%; 95%CI 7.9 to 23.7; p,0.001) pre-treatment viral load strata. The initial regimen type selected was found to significantly interact with the relationship of third drug class (p = 0.040), dosing requirements (p = 0.024), previous AIDS events (p = 0.003), and pre-treatment CD4 lymphocyte count (p = 0.049) on efficacy. On regression analysis (Table 7), the pre-treatment CD4 count was associated with efficacy within ‘Preferred’ regimens (coefficient ,
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Efficacy of Initial Antiretroviral Therapy: A Systematic Review
0.1%; 95%CI 0.0 to 0.1; p = 0.035); that is, efficacy was ,0.1% greater for every one cell increase in the absolute CD4 count. Amongst ‘Alternative’ regimens (Table 8), previous AIDS events were associated with lesser efficacy (coefficient 20.4%; 95%CI 20.6 to 20.1; p = 0.011), INSTI use with greater efficacy (vs. NNRTI; coefficient 15.3%; 95%CI 8.7 to 21.9; p,0.001), and a requirement for fasting when taking medications was associated with lesser efficacy (vs. no dosing requirement; coefficient 210.6%; 95%CI 217.7 to 23.4; p = 0.005).
decision (8.8%, SD 5.4) was the most common reason, followed by adverse events (6.5%, SD 4.0) and virological failure (4.3%, SD 4.3). Multivariable analysis of studies commenced 2005 or later resulted in similar findings to the primary analysis. Tenofoviremtricitabine continued to be associated with higher efficacy than abacavir-lamivudine (coefficient 8.8%; 95%CI 3.9 to 13.8; p = 0.001), as was INSTI over NNRTI (coefficient 5.5%; 95%CI 0.1 to 11.0; p = 0.050).
Efficacy stratified by pre-treatment viral load
Other sensitivity analyses
For high ($100,000 copies/mL) and low (,100,000 copies/ mL) viral load strata, 98 groups (45%, 22,878 participants, mean 81 weeks’ follow-up [SD 36]) had efficacy data available. Mean efficacy was 62% (SD 15) and 70% (SD 15), respectively (difference 8.4%; 95%CI 6.0 to 10.9; p,0.001). Stratification by pre-treatment viral load showed no significant interaction with the relationship of any pre-selected variable upon efficacy (p.0.05), and univariable-multivariable analysis was not pursued. Despite this lack of interaction, ‘Preferred’ regimens (February 2013 DHHS guidelines) maintained greater efficacy at both higher and lower viral load strata; efficacy within these strata were 73% (SD 12) and 82% (SD 12), respectively (difference 9.1%; 95%CI 4.3 to 14.0; p = 0.001).
The separate analyses conducted after excluding cohorts and abstracts, and then including the year of commencement as a variable both gave similar results to the primary analysis (data not shown).
Discussion For studies initiated from 1995 through to 2010, the overall mean efficacy of initial cART was low (60% over 82 weeks), although it has risen substantially for more recent studies, suggesting it could rise further with studies commenced post2010 (not available for this analysis before locking of the final database). Efficacy was higher (75% over 99 weeks) with current ‘Preferred’ cART regimens. There is ongoing loss of efficacy through the second and third years of initial cART, mainly because of participant decision or adverse events. Across all analyses, tenofovir-emtricitabine was more effective than abacavirlamivudine, and INSTI more effective than NNRTI. As the sensitivity analyses demonstrated, this also applied to the more recent studies of initial cART. Participants with pre-treatment viral load ,100,000 copies/mL plasma had significantly better antiviral outcomes on initial cART, a finding persisting within ‘Preferred’ regimens, and similar in magnitude to that favouring February 2013 DHHS ‘Preferred’ over ‘Alternative’ regimens. Initial cART should aim to induce and maintain long-term virological control. For a disease that presently requires life-long treatment, this analysis demonstrates suboptimal efficacy and durability even with currently ‘Preferred’ initial regimens, corroborating similar findings of the Antiretroviral Therapy Cohort Collaboration [13]. In practice, individuals failing one regimen are likely to be switched to a different, effective combination. Therefore, the present data reflect success or failure of initial cART only, not the absolute failure of all treatment, which we have not examined. Nevertheless, our data show for the first time that almost 15% of participants failed cART between weeks 48 and 144 of therapy. If this trend were ongoing, the majority of initial regimens would fail within 10 years of commencement. As the pace of new antiretroviral development slows, treatment options may become limited as patients are progressively exposed to switching of cART regimens, even in resource-rich settings [14]. Participant decision was the most common identifiable cause of failure, ahead of adverse events or virological failure. This was the case both for studies of ‘Preferred’ regimens, and more recent studies (post-2005). The underlying reasons for this decision, or whether participants re-started cART outside a study, were not routinely reported. Uncovering and systematically documenting reasons for premature cART cessation and subsequent outcomes will be necessary if participants are to be retained more successfully on treatment. Industry-sponsored studies were associated with lower rates of premature cessation due to participant decision or adverse events. It is possible that the cost-free medication and more frequent clinical follow-up commonly
Premature treatment cessation: all studies An average of 25% (SD 11) of participants prematurely ceased initial cART. The most common reason was participant decision (11%, SD 6.6) followed by adverse events (8.1%, SD 5.9); virological failure was less common (3.5%, SD 4.0). Total premature cessation was lower with more recent studies (Figure 3). Most cessations (20%, SD 9.0) occurring through week 48, 9.2% (SD 11.0) ceasing between weeks 48 and 96, and a further 4.9% (SD 2.7) ceasing between weeks 96 and 144. On multivariable analysis adjusting for study, participant, disease and treatment characteristics and adverse events, industry-only sponsorship was associated with lower cessation rates due to participant decision, compared to industry-supported academic (coefficient 22.6%; 95%CI 24.8 to 20.5; p = 0.015) and academic-only sponsorships (coefficient 24.0%; 95%CI 26.3 to 21.6; p = 0.001). Similar associations existed between sponsorship and cessation due to adverse events. Phase 2 studies were associated with lower rates of cessation due to adverse events than phase 3 or 4 studies (coefficients 22.6% and 24.0%, respectively; p,0.015). Neither NRTI backbone nor third drug class were associated with cessation attributed either to adverse events or participant decision.
Studies commenced 2005 and later The pre-planned sensitivity analysis of the most recent studies (commenced 2005 and later) included 82 groups. Of these, 76 (93%) were randomised trials and 6 (7%) were prospective cohorts, encompassing 16,795 participants (median 118 participants/ group; interquartile range 47 to 170). Participant characteristics were similar to that of the primary analysis (mean age 37 years [SD 1.9]; mean CD4 count 238 cells/mL [SD 80]; mean HIV-1 plasma viral load log10 4.9 [0.3]). Mean overall efficacy was higher than for all included studies: 71% (SD9.6) over a mean follow-up of 86 weeks (SD 37). Efficacy at 96 and 144 weeks was reported for 37 (45%, 9,961 participants) and 12 (15%, 3,486 participants) groups, respectively. Mean efficacy at 48 weeks was 77% (SD 8.6), 70% (SD 11) at 96 weeks, and 66% (SD 6.3) at week 144. Total mean premature treatment cessation was 21% (SD 8.9). Like the primary analysis, participant PLOS ONE | www.plosone.org
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Efficacy of Initial Antiretroviral Therapy: A Systematic Review
Table 3. Study characteristics.
Characteristics
Studies (n = 114)
Groups (n = 216)
Participants (n = 40,124)
Weighted proportion (%)
Study design Randomised
Placebo
Phase*
Sponsorship
Year commenced
Recruitment geography
Maximum follow-up
Viral load endpoint
ITT analysis
Publication type
no
17
20
3,590
8.9
yes
97
196
36,534
91.1
no
79
148
22,494
56.1
yes
35
68
17,630
43.9
2
29
50
4,386
10.9
3
48
96
26,325
65.6
4
37
70
9,413
23.5
academic
30
55
10,681
26.6
industry
63
123
26,547
66.2
industry + academic
21
38
2,896
7.2
pre-1997
6
10
641
1.6
1997–1999
27
53
8,108
20.2
2000–2002
23
43
9,331
23.3
2003–2005
32
61
13,062
32.6
2006–2008
16
31
6,597
16.4
2009–
9
17
2,346
5.8
Africa
109
204
36,736
91.6
The Americas
97
179
31,468
78.4
Asia
104
194
35,100
87.5
Australia/Europe
100
186
33,342
83.1
48 weeks
73
131
20,165
50.3
96 weeks
29
60
11,629
29.0
144 weeks
12
25
8,330
20.8
,50
103
196
34,500
86.0
other{
11
20
5,624
14.0
ITT M = F
36
62
7,127
17.8
ITT NC = F
44
84
13,676
34.1
TLOVR
34
70
19,321
48.2
abstract only
11
22
5,257
13.1
journal
103
194
34,867
86.9
Eligibility criteria ALT/AST
CD4 count
Genotype
Haemoglobin
Viral load
History of IDU
Previous AIDS
restricted
88
173
31,536
78.6
unrestricted
26
43
8,588
21.4
restricted
64
122
19,428
48.4
unrestricted
50
94
20,696
51.6
restricted
31
62
12,563
31.3
unrestricted
83
154
27,561
68.7
restricted
67
133
24,224
60.4
unrestricted
47
83
15,900
39.6
restricted
92
184
33,915
84.5
unrestricted
22
32
6,209
15.5
restricted
2
5
223
0.6
unrestricted
112
211
39,901
99.4
restricted
6
11
1,436
3.6
unrestricted
108
205
38,688
96.4
*Prospective cohort studies were considered phase 4 studies. { Includes ,400, ,200, ,40 and ,20 copies/mL. ITT, intention-to-treat; M = F, missing equals failure; NC = F, non-completer equals failure; TLOVR, time to loss of virological response; ALT, alanine transaminase; AST, aspartate transaminase; IDU, injection drug use; AIDS, acquired immune deficiency syndrome. doi:10.1371/journal.pone.0097482.t003
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Efficacy of Initial Antiretroviral Therapy: A Systematic Review
Table 4. Treatment and participant characteristics: all groups.
Groups (n = 216)
Participants (n = 40,124)
Mean (across groups)
Mean (weighted)
Pills per day, mean (SD)
216
40,124
6.8 (3.8)
6.3 (3.6)
Doses per day, mean (SD)
216
40,124
2.0 (0.7)
2.0 (0.7)
fasting only
59
9,754
27.3
24.3
fasting + food
22
5,108
10.2
12.7
food only
65
11,947
30.0
29.8
no relation to food
70
13,315
32.4
33.2
Characteristics Antiretroviral therapy
Dosing with food, %
NRTI backbone, %
Third drug class, %
TDF-FTC
47
11,001
21.7
27.4
AZT-3TC
56
10,832
25.9
27.0
d4T-3TC
27
3,988
12.5
9.9
ABC-3TC
26
5,516
12.0
13.7
d4T-ddI
20
2,349
9.3
5.9
Nil/2 NRTIs
20
4,064
9.3
10.1
TDF-3TC
7
1,092
3.2
2.7
ddI-3TC
8
802
3.7
2.0
AZT-ddI
2
116
0.9
0.3
ddI-FTC
1
286
0.5
0.7
3TC
1
39
0.5
0.1
TDF-ELV*
1
39
0.5
0.1
NNRTI
94
19,512
43.5
48.6
PI (boosted)
56
9,724
25.9
24.2
PI (unboosted)
38
5,686
17.6
14.2
NRTI/2 NRTIs
12
1,771
5.6
4.4
INSTI
9
2,150
4.2
5.4
INSTI+PI
3
239
1.4
0.6
NNRTI+PI
2
679
0.9
1.7
CCR5/CCR5+PI
2
363
0.9
0.9
Participants at baseline Age, years (SD)
207
39,524
37 (2.1)
37 (2.1)
Gender, % (SD)
male
213
40,022
76 (14)
76 (15)
Race, % (SD)
white
170
33,524
64 (22)
65 (17)
black
166
33,803
25 (19)
27 (17)
other
163
32,689
12 (21)
9.8 (14)
MSM
88
14,596
48 (20)
52 (19)
IDU
104
18,287
12 (11)
9.8 (9.2)
heterosexual
83
14,261
39 (18)
38 (15)
other
80
13,939
5.2 (8.3)
4.7 (6.5)
HIV risk factor, % (SD)
Previous AIDS event, % (SD)
161
30,035
14 (19)
12 (13)
Pre-treatment CD4, % (SD)
214
40,048
265 (103)
248 (81)
log
210
39,808
4.8 (0.3)
4.9 (0.2)
% $100,000 cp/mL
150
31,409
41 (14)
43 (11)
Hepatitis B sAg +, % (SD)
129
26,739
3.2 (2.8)
3.4 (2.3)
Hepatitis C Ab +, % (SD)
124
25,487
11 (11)
10 (8.8)
Weight, kg (SD)
112
22,196
71 (5.6)
71 (5.1)
Pre-treatment HIV viral load
*ELV has not received regulatory approval. This group was excluded from the analysis. SD, standard deviation; TDF, tenofovir; FTC, emtricitabine; AZT, zidovudine; 3TC, lamivudine; d4T, stavudine; ddI, didanosine; ABC, abacavir; ELV, elvucitabine; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; INSTI, integrase strand transfer inhibitor; CCR5, chemokine receptor 5 inhibitor; HIV, human immunodeficiency virus; MSM, men who have sex with men; IDU, injecting drug use; AIDS, acquired immune deficiency syndrome; Ab, antibody; sAg, surface antigen; cp, copies. doi:10.1371/journal.pone.0097482.t004
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Efficacy of Initial Antiretroviral Therapy: A Systematic Review
it may help explain the lesser efficacy of abacavir-lamivudine compared to tenofovir-emtricitabine – a finding noted in the ACTG A5202 study [17]. In the existing pre-planned multivariable analyses, neither median viral load, nor the proportion of participants with a pretreatment viral load $ or ,100,000 copies/mL was significantly associated with efficacy in the overall multivariable analysis. Within the pre-planned subgroup analyses, there were also no significant interactions between: (1) the regimen type and pretreatment viral load strata; or (2) the viral load-stratified efficacy and treatment characteristics. However, a post-hoc analysis of our data comparing tenofovir-emtricitabine with abacavir-lamivudine mirrors the gaps in efficacy seen between ‘Preferred’ and ‘Alternative’ regimens (Table S1 in Appendix S1: Supplementary post-hoc stratified analyses). At viral loads $ 100,000 copies/mL, overall efficacy with tenofovir-emtricitabine was higher than abacavir-lamivudine (71% [SD 10] vs. 59% [SD 12], respectively; difference 11%; 95%CI 3.6 to 17.7; p = 0.004), with a similar difference at viral loads ,100,000 copies/mL (79% [SD 12] vs. 67% [SD 14], respectively; difference 14%; 95%CI 6.5 to 20.8; p,0.001). The mean follow-up periods were 83 (SD 37) and 74 (SD 38) weeks, respectively. The descriptive findings, both pre-planned and post-hoc, suggest that the overall superior efficacy of tenofovir-emtricitabine (and ‘Preferred’ regimens) is independent of the plasma viral load and further implies that for a given drug or combination, efficacy at lower viral loads is better than at higher viral loads. Not only does this validate current ‘Preferred’ regimens, it argues for future guidelines recommending cART initiation when the plasma viral load rises towards 100,000 copies/ mL. Comparable results have been reported recently, albeit limited to 48 weeks’ follow-up [18]. The ACTG A5202 study showed a higher risk of study-defined virological failure with abacavir-lamivudine for viral loads $100,000 copies/mL at interim analysis (resulting in the unblinding of that stratum), rather than all-cause, intention-to-treat failure. As we did not have premature cessation data stratified by pre-treatment viral load, the results are not directly comparable. While viral load and regimen type did not significantly interact to influence efficacy, precluding multivariable analyses of these subgroups, it is worth noting that the high-low threshold of 100,000 copies/mL (log105.0) reported in studies is arbitrary. A meta-analysis of efficacy data from individual participants may reveal a clinically relevant association between gradations of viral load and long-term efficacy. The superiority of tenofovir-emtricitabine over abacavir-lamivudine, although statistically significant on primary analysis, remains confounded by one major issue – that of abacavir-related hypersensitivity. The association between HLA-B*5701 and abacavir-related hypersensitivity, first reported in 2002, is welldescribed [19,20]. It would have been advantageous to have more efficacy data with pre-treatment HLA-B*5701 screening. However, due to limited availability, testing for HLA-B*5701 did not become the standard of care until its inclusion in DHHS guidelines from 2007 [21]. By that point, 24 of the 26 groups using abacavir in our review had already commenced, leaving only two studies (total 227 participants) which utilised HLA-B*5701 screening (efficacy 55% [SD 13] over 96 weeks). Frequency of abacavir-related hypersensitivity is estimated at between 2% and 9%, with some ethnic variation [22]. A metaanalysis of 5,332 patients exposed to abacavir reported a mean incidence of 4% (range 3% to 6%) [23]. Hypersensitivity does contribute to the lesser efficacy of abacavir-lamivudine vs. tenofovir-emtricitabine in our primary analysis, but within the limitations of the source data its relative contribution to higher
Figure 2. Efficacy by year of study commencement. Summary bubble plot displaying the change in weighted intention-to-treat antiviral efficacy of initial antiretroviral therapy by the year included studies were commenced. Each bubble represents an individual treatment group, proportional to size. SD, standard deviation. doi:10.1371/journal.pone.0097482.g002
mandated within such studies may have attenuated cessation rates. Treatment cessation due to virological failure was consistently low. This was the case even in the earliest cART studies, possibly because the limited treatment options available at the outset of cART prevented drug switches even in the event of study-defined virological failure. Most treatment failure/cessation occurred early, within the first 48 weeks, with incremental declines of ,10% per year at weeks 96 and 144. It may seem self-evident that the longer an individual remains on a single regimen the lower the likelihood of failure, but this may explain why at week 96, only study design characteristics were associated with efficacy, rather than treatment or disease characteristics. There were, however, considerably fewer study groups with data for 96 weeks of follow-up. Unsurprisingly, of the intention-to-treat strategies used to calculate efficacy, the ‘missing equals failure’ algorithm was associated with greater treatment success, as it has the fewest imputations regarding failure. This raises the possibility that overall efficacy may have been even lower if other, more stringent algorithms had been used exclusively. However, the ‘missing equals failure’ approach arguably more closely reflects actual clinical practice. The four key international guidelines use the CD4 lymphocyte count as a key marker for when to start cART, with none recommending pre-treatment viral load as an indication [8,9,15]. Viral load was removed from DHHS recommendations in 2007, because of data indicating that risk of AIDS or death in individuals receiving cART with pre-treatment CD4 counts $350 cells/mL was ,2% regardless of viral load [16]. Our findings that: (1) the high-vs.-low viral load and ‘Preferred’-vs.-‘Alternative’ regimen efficacy differences were similar (8.4% vs. 10%, respectively); and (2) ‘Preferred’ regimens had greater efficacy at pre-treatment viral loads ,100,000 copies/mL, are therefore striking, since it suggests that the pre-treatment viral load exerts an independent effect upon efficacy, even as guidelines place a primary emphasis on regimen selection. An implication of this finding is that the presence or absence of pre-treatment viral loads $100,000 copies/mL may influence the efficacy ultimately reported in studies. In particular,
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8.1 (5.9)
3.5 (4.0)
4.7 (4.1)
25 (11)
Adverse events
Virological failure
Other
Total 20 (9.0)
3.3 (3.2)
2.4 (3.0)
6.9 (5.4)
8.5 (4.8)
28 (11)
6.4 (4.2)
4.8 (4.9)
7.5 (5.3)
14 (7.9)
60 (16)
96 (-)
85 (19,959)
34 (9.6)
8.3 (6.2)
4.4 (2.7)
10 (5.6)
15 (8.5)
52 (18)
144 (-)
25 (8,330)
No data
No data
No data
No data
No data
70 (15)
81 (36)
98 (13,184)
*According to United States Department of Health and Human Services February 2013 Adult and Adolescent Guidelines. { Data for premature cessation stratified by pre-treatment viral load not available. SD, standard deviation. doi:10.1371/journal.pone.0097482.t005
11 (6.6)
Participant decision
Premature treatment cessation, % (SD){
66 (16)
60 (16)
Antiviral efficacy, % (SD)
216 (40,124) 48 (-)
216 (40,124)
No data
No data
No data
No data
No data
62 (15)
81 (36)
98 (9,694)
$100,000
,100,000
144 weeks
48 weeks
96 weeks
Pre-treatment HIV viral load (copies/mL plasma)
Maximum duration of follow-up
Mean follow-up, weeks (SD) 82 (38)
Groups (participants)
All groups
Table 5. Efficacy and categories of premature treatment cessation.
20 (8.1)
3.4 (2.7)
3.6 (4.4)
5.3 (2.8)
9.0 (4.6)
75 (7.9)
99 (41)
27 (5,677)
Preferred*
25 (7.8)
4.5 (2.2)
2.8 (3.5)
7.3 (4.0)
11 (5.2)
65 (6.6)
86 (35)
36 (8,556)
Alternative*
DHHS recommendation (February 2013)
27 (12)
5.0 (4.8)
3.7 (4.0)
9.0 (6.7)
11 (7.3)
55 (17)
77 (37)
153 (25,891)
Non-Preferred/NonAlternative*
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13
Third drug class
213.3 18.5
51 (12) 84 (4.7) 64 (3.6) 40 (16) 61 (4.7)
INSTI
INSTI+PI
NNRTI+PI
CCR5/CCR5+PI
20.9
221.2
0.9
219.2
NRTI/2 NRTIs
2.9
67 (8.6) 42 (11)
PI (boosted)
PI (unboosted)
Ref
25.9
5.1
230.5
28.3
22.6
26.1
225.3
210.4
215.0
61 (15)
78 (-) 67 (-)
ddI-FTC
3TC
NNRTI
65 (8.4) 42 (3.5)
ddI-3TC
66 (15) 69 (4.6)
Nil/2 NRTIs
TDF-3TC
AZT-ddI
63 (6.8) 44 (13)
ABC-3TC
d4T-ddI
55 (12)
d4T-3TC
219.6
Ref
73 (10) 48 (15)
TDF-FTC
NRTI backbone
AZT-3TC
21.7
6.9
Ref
214.5
Ref
1.1
24.7
Ref
29.0
29.0
66 (11)
unrestricted
Ref 26.0
Pills per day
54 (18)
55 (15)
restricted
72 (10)
restricted
64 (15)
TLOVR
unrestricted
59 (15)
ITT NC = F
54 (16)
4 53 (17)
62 (16)
ITT M = F
64 (15)
2
3
Coefficient, %`
Doses per day
Treatment
CD4 count
Genotype
Eligibility criteria
ITT analysis
Phase
Study design
Efficacy, % (SD)
217.4, 15.5
236.8, 25.6
213.3, 15.2
10.7, 26.2
220.4, 26.3
223.7, 214.8
21.0, 6.8
233.2, 21.5
218.3, 28.5
249.2, 211.7
218.0, 1.5
211.3, 6.2
212.6, 0.3
232.0, 218.7
216.3, 24.5
221.0, 29.1
224.4, 214.7
211.5, 26.6
22.2, 21.2
3.0, 10.8
218.5, 210.6
24.0, 6.2
29.7, 0.3
214.5, 23.5
211.0, 20.9
95%CI
Univariable Analysis
0.911
0.008
0.897
,0.001
,0.001
,0.001
0.140
0.671
0.668
0.002
0.097
0.567
0.063
,0.001
0.001
,0.001
,0.001
,0.001
,0.001
0.001
,0.001
0.662
0.064
0.001
0.022
p
Table 6. Characteristics* associated with efficacy of initial antiretroviral therapy: all groups.
,0.001
,0.001
,0.001
,0.001
0.001
,0.001
0.035
0.006
p trend
43.0
35.3
22.3
19.2
6.2
21.7
2.1
3.8
r2 (%)
22.7
227.3
26.8
11.9
210.7
215.0
20.9
Ref
29.8
7.9
235.3
27.9
21.2
20.7
218.4
27.6
211.4
213.3
Ref
27.5
26.4
Ref
Coefficient, %`
217.5, 12.2
241.9, 212.6
220.7, 7.0
4.6, 19.2
217.4, 24.1
219.4, 210.6
24.7, 3.0
233.9, 14.3
211.6, 27.4
251.9, 218.7
216.6, 0.8
28.5, 6.2
26.9, 5.4
224.9, 212.0
212.7, 22.6
216.8, 25.9
218.0, 28.5
211.5, 23.6
210.1, 22.7
95%CI
Multivariable Analysis{
0.723
,0.001
0.331
0.002
0.002
,0.001
0.660
0.424
0.426
,0.001
0.075
0.758
0.818
,0.001
0.003
,0.001
,0.001
,0.001
0.001
p
,0.001
,0.001
,0.001
p group
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*Study phase, genotype/CD4 eligibility restrictions, pills and doses per day were significant on univariable analysis but not multivariable. Co-infection with hepatitis B or C were excluded from the multivariable analysis because . 20% of groups were missing data. { Study sponsorship, placebo use, country/region of recruitment, haemoglobin/viral load/liver function eligibility restrictions, race, risk factors for HIV infection, sex, previous AIDS events, pre-treatment viral load/CD4 count, dosing relative to food, serious adverse events, and clinical/laboratory adverse events of at least moderate severity were variables not significant on univariable analysis. ` Coefficient represents the adjusted percentage difference in outcomes relative to a unit increase in any variable (or relative to the reference variable within a category). SD, standard deviation; CI, confidence interval; ITT, intention-to-treat; M = F, missing equals failure; NC = F, non-completer equals failure; TLOVR, time to loss of virological response; TDF, tenofovir; FTC, emtricitabine; AZT, zidovudine; 3TC, lamivudine; d4T, stavudine; ddI, didanosine; ABC, abacavir; ELV, elvucitabine; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; INSTI, integrase strand transfer inhibitor; CCR5, chemokine receptor 5 inhibitor. doi:10.1371/journal.pone.0097482.t006
Efficacy of Initial Antiretroviral Therapy: A Systematic Review
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treatment failure cannot be quantified. Given the insoluble nature of the missing data, one approach to addressing this problem is to infer that the adjusted efficacy difference of 10% between tenofovir-emtricitabine and abacavir-lamivudine is partially due to hypersensitivity (assuming a mean incidence of 4%), with other factors responsible for the balance. For instance, amongst post2005 studies, the approval of single-tablet, fixed-dose preparations is a possible reason for patients electing to switch away from abacavir mid-clinical trial. During the period of analysis (up to 2010), all such preparations contained tenofovir-emtricitabine as the NRTI backbone. The pairing of tenofovir with emtricitabine, rather than lamivudine, may also contribute to the efficacy difference. Although emtricitabine and lamivudine have closely related chemical structures [24,25], and are regarded as interchangeable by the DHHS and the World Health Organization [8,26], emtricitabine has a longer intracellular half-life than lamivudine [27,28]. The alternative approach is a separate, post-hoc, multivariable analysis excluding the 24 abacavir-lamivudine groups (5,289 participants) that did not use HLA-B*5701 screening. Such an analysis was performed (Table S2 in Appendix S1: Supplementary post-hoc stratified analyses); in this model, the comparative difference in efficacy (vs. tenofovir-emtricitabine) was non-significant (coefficient 214.4%; 95%CI 230.2 to 2.5; p = 0.175), although the large negative coefficient favoured tenofovir-emtricitabine. In one of the two included abacavir groups (from study CNA109586), hypersensitivity accounted for more premature cessation in the abacavir arm than the comparator tenofovir-emtricitabine arm (6% vs. ,1%, respectively), despite HLA-B*5701 screening [29]. With only the two HLAB*5701-screened groups included, this post-hoc analysis is underpowered to compare abacavir-lamivudine with other NRTI backbones. However, as all other groups were left unchanged from the original primary analysis, the other predictive associations (third drug class, intention-to-treat analysis method) were preserved. The disadvantage of removing the majority of abacavirlamivudine groups is that it also removes a large amount of data for the concomitant drug classes from our study. If further studies were then to be excluded on the basis either of currently nonrecommended regimens or a non-standard practice, it would also necessitate the exclusion of older cART regimens (e.g. zidovudinelamivudine), and similarly, the many earlier studies commenced before pre-treatment resistance genotyping became the recommended standard of care in 2006 [30]. With the removal of such a large number of groups from the analysis, the benefits of ecological analysis may be lost, and with it, several comparisons between NRTI backbones and third drug classes. This systematic review only evaluated entire third drug classes. While this may limit the specificity of the comparisons, assessing individual drugs will result in a loss of analytical power. Our approach is vindicated by long-term prospective data. The adjusted efficacy difference between INSTI and NNRTI classes is consistent with the 10% lower efficacy of efavirenz vs. raltegravir (both ‘Preferred’ agents), as well as with recently-published fiveyear outcomes of the 004 and STARTMRK studies, and the short-term results of the SPRING-1 study comparing efavirenz with dolutegravir [37,38,39]. Examining pooled data can effectively portend long-term prospective results, displaying the value of systematic review. The favourable efficacy of INSTI- over NNRTI-based initial cART has been noted elsewhere [31]. Our analysis supports this finding, but extends it to include a treatment advantage over boosted PI therapy, and updates earlier meta-analyses conducted 14
May 2014 | Volume 9 | Issue 5 | e97482
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Fasting + food
,0.1
7.6
Pre-treatment CD4
82 (7.9) 0.0
3 (829)
3.2
Ref
8.6
7.6
3.1
Ref
Coefficient, %
Previous AIDS
INSTI
75 (6.8)
72 (7.6)
79 (4.5)
77 (8.7)
71 (7.0)
71 (7.9)
Efficacy, % (SD)
0.0, 0.1
20.2, 0.2
22.5, 17.6
23.7, 10.2
23.4, 20.6
22.9, 18.1
27.6, 13.8
95%CI
0.035
0.784
0.134
0.351
0.152
0.147
0.552
p
0.035
0.784
0.282
0.323
p trend
17.6
25.8
5.9
5.4
r2 (%)
,0.1
Coefficient, %
0.0, 0.1
95%CI
Multivariable Analysis{
0.035
p
0.035
p group
*Dosing relative to food, third drug class, previous AIDS events and pre-treatment CD4 interacted significantly with DHHS regimen type to affect efficacy. { Dosing relative to food, NRTI backbone, third drug class and previous AIDS events were not significantly associated with efficacy on univariable analysis. DHHS, United States Department of Health and Human Services; SD, standard deviation; CI, confidence interval; TDF, tenofovir; FTC, emtricitabine; 3TC, lamivudine; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; INSTI, integrase strand transfer inhibitor. doi:10.1371/journal.pone.0097482.t007
Participant
10 (2,119)
PI (boosted)
14 (2,729)
9 (1,620) 10 (2,164)
Fasting only
Food only
3 (886)
None
Third drug class NNRTI
Dosing requirements
Groups (participants)
Univariable Analysis
Table 7. Characteristics* associated with efficacy of initial antiretroviral therapy: February 2013 DHHS ‘Preferred’ regimens (27 groups).
Efficacy of Initial Antiretroviral Therapy: A Systematic Review
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Participant
*Dosing relative to food, third drug class, previous AIDS events and pre-treatment CD4 interacted significantly with DHHS regimen type to affect efficacy. { NRTI backbone and pre-treatment CD4 count were not significantly associated with efficacy on univariable analysis. DHHS, United States Department of Health and Human Services; SD, standard deviation; CI, confidence interval; TDF, tenofovir; FTC, emtricitabine; 3TC, lamivudine; ABC, abacavir; NRTI, nucleoside reverse transcriptase inhibitor; NNRTI, non-nucleoside reverse transcriptase inhibitor; PI, protease inhibitor; INSTI, integrase strand transfer inhibitor. doi:10.1371/journal.pone.0097482.t008
0.019 0.019 20.7, 20.1 20.4 76.5
25.9 0.003
,0.001 ,0.001
0.003 21.2, 20.3
0.1, 0.1
20.7
0.1
Previous AIDS
,0.001 ,0.001
0.527 26.4, 3.3
10.6, 26.4 18.4
21.5 64 (5.6) 24 (5,361)
4 (784)
PI (boosted)
INSTI
85 (2.3)
Ref 67 (7.8) 11 (3,160) NNRTI Third drug class
Pre-treatment CD4
,0.001 0.919
,0.001 8.7, 21.9
24.8, 5.3 0.3
51.7
15.3
Ref
0.060
0.226 22.7, 10.8 45.7 0.003 4.9, 22.1 13.5 3 (1,082) Fasting + food
81 (4.6)
0.051 210.0, 0.0 25.0 18 (4,603) Food only
64 (7.8)
Ref
3 (720)
68 (4.2) 15 (2,900) None Dosing requirements
Fasting only
57 (4.8)
212.5
221.7, 23.4 0.008
,0.001
p trend p 95%CI Coefficient, % Efficacy, % (SD) Groups (participants)
4.1
29.0, 0.2 24.4
Ref
210.6
95%CI Coefficient, %
r2 (%)
Multivariable Analysis{ Univariable Analysis
Table 8. Characteristics* associated with efficacy of initial antiretroviral therapy: February 2013 DHHS ‘Alternative’ regimens (39 groups).
p
217.7, 23.4 0.005
0.004
p group
Efficacy of Initial Antiretroviral Therapy: A Systematic Review
16
prior to INSTI availability. In order to investigate whether the efficacy results for INSTI-based regimens were inflated by the presence of short-term efficacy data for elvitegravir/cobicistat and dolutegravir, a stratified post-hoc analysis was performed separating raltegravir (5 groups, 1,246 participants) from the other INSTI drugs (4 groups, 904 participants). No studies from the primary analysis were excluded. Efficacy overall and at weeks 49 and 96 were similar for raltegravir and non-raltegravir INSTIs (Table S3 in Appendix S1: Supplementary post-hoc stratified analyses), although follow-up was shorter for the latter (83 weeks [SD 51] vs. 56 weeks [SD 21]). When multivariable analysis was performed using raltegravir and non-raltegravir INSTIs as separate categorical variables, the results were similar to those of the primary analysis (Table S4 in Appendix S1: Supplementary post-hoc stratified analyses). Both raltegravir (coefficient 10.9%; 95%CI 1.2 to 20.6; p = 0.028) and non-raltegravir INSTIs (coefficient 13.0%; 95%CI 2.6 to 23.4; p = 0.014) were superior third drug options compared to the reference (NNRTIs). These results imply similar efficacy within the INSTI class, a position since adopted by the DHHS in October 2013, when all three INSTIs became listed as ‘Preferred’ options [32]. In the primary analysis, unboosted PIs (regarded as a separate third drug class) were inferior to NNRTIs, while boosted PIs and NNRTIs were similar. The DHHS guidelines do not sanction the use of any unboosted PI, with the exception of unboosted atazanavir, which was used by five groups (932 participants) in our analysis. Unboosted atazanavir was reported as non-inferior when compared to efavirenz and atazanavir/ritonavir in the AI424-034 and ARIES studies, respectively [33,34]. However, intention-to-treat efficacy in AI424-034 was low (32% at 48 weeks), while the ARIES study used 36 weeks of atazanavir/ ritonavir as an induction therapy prior to switching to unboosted atazanavir. Thus whilst atazanavir/ritonavir is a DHHS ‘Preferred’ third drug, unboosted atazanavir is listed as a ‘less satisfactory’ treatment option, one that is neither ‘Preferred’ nor ‘Alternative’ [1]. We conducted a post-hoc analysis considering unboosted atazanavir as a separate third drug class. The results of this analysis were similar to the primary analysis (Table S5 in Appendix S1: Supplementary post-hoc stratified analyses). Unboosted atazanavir remained significantly associated with lesser efficacy, as did the other unboosted PIs. The stratified descriptive data supports this; compared to the overall efficacy of unboosted PIs in the primary analysis (42% [SD 11]), the efficacy of unboosted atazanavir and other unboosted PIs were similar 2 40% (SD 12) and 42% (SD 11), respectively (Table S6 in Appendix S1: Supplementary post-hoc stratified analyses). In contrast, the efficacy of atazanavir/ritonavir-based regimens was 72% (SD 8). This supports the findings of the ACTG A5175 study, where the unboosted atazanavir arm was stopped early because of inferior efficacy [35]. The pharmacokinetics of unboosted atazanavir is another reason for caution, as its use effectively precludes combination with tenofovir-containing NRTI backbones, as the latter appears to reduce the bioavailability of atazanavir significantly [1,36]. In the groups examined in this systematic review, unboosted atazanavir was paired with NRTI backbones selected from older, less effective agents (didanosine, stavudine, zidovudine), which may further attenuate its usefulness in clinical practice. Despite the advent of fixed-dose combinations, neither daily pill count nor doses significantly predicted efficacy, even amongst ‘Preferred’ or ‘Alternative’ regimens. However, such fixed-dose combinations are a relatively recent development (only three groups had a daily pill count of one), so there were insufficient data to address this question adequately. From our results, however, it May 2014 | Volume 9 | Issue 5 | e97482
Efficacy of Initial Antiretroviral Therapy: A Systematic Review
Figure 3. Premature cessation of initial antiretroviral therapy by year of study commencement. (A) Total cessation from all causes; (B) attributed to participant decision – withdrawal from study or lost to follow-up; (C) attributed to adverse events; and (D) due to study-defined virological failure. Studies were included in this analysis if total premature cessation was reported. SD, standard deviation. doi:10.1371/journal.pone.0097482.g003
other, more commonly-reported meta-analyses of cART aim to combine similarly-designed, randomised comparisons of efficacy/ failure to achieve data homogeneity. Such analyses therefore have a different hypothesis, i.e. they evaluate the comparison, whereas a study such as ours describes overall efficacy and failure in each group exposed to the treatment drug(s). By including a wide variety of treatments and settings, our data set is more heterogeneous; a meta-regression approach permits exploration of potential sources of heterogeneity in the estimation of efficacy. This would not be possible with a ‘typical’ meta-analysis of similar studies. However, because broad associations are identified, it is not possible to infer causality, and it would be incorrect to deduce the impact of a particular cART regimen on efficacy in participants of a certain age, gender, race, location or clinical status. However, as standardised outcome measures (virological efficacy and failure) have been applied to a large weighting of participants, it is best suited to representing the likely outcomes for populations with a similar profile, reporting associations that may advise public policy, like the DHHS and other international antiretroviral guidelines, and identifying topics for future study. As most study groups were of predominantly white race, this may limit applicability to currently resource-limited settings.
appears that while a one-pill-per-day regimen is convenient, it is not necessarily superior. This remains an inference, as importantly, 44% of studies (weighted) used placebo controls, inflating the daily pill count. We did not analyse ‘Preferred’ and ‘Alternative’ cART regimens by guidelines other than of the DHHS (e.g. World Health Organisation, International Antiviral Society – USA, European AIDS Clinical Society). International ART guidelines have all been derived from the same pool of publicly reported data. Such duplicated analyses are likely to show results similar to the ones we obtained for DHHS-specified cART regimens, i.e. that use of a ‘Preferred’ cART regimen (however defined) resulted in higher efficacy than with ‘Alternative’ regimens. Similarly, despite our updated search being restricted to between 2008 and 2012, an additional sensitivity analysis for post-2008 studies was not performed. This would have effectively duplicated the ‘Preferred’-vs.-‘Alternative’ regimen analysis, while arbitrarily excluding studies of currently ‘Preferred’ (and therefore relevant) regimens that commenced pre-2008. There are several limitations to our study. Chief of these is that the base unit of analysis in our methodology was the treatment group. Hence ours was an ecological analysis, using mean aggregate data, rather than individual participant data. Also,
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A multivariable approach was used to interrogate heterogeneity within the efficacy data. With this approach, bias due to missing data may allow some clinically relevant sources of heterogeneity to be missed whilst other, non-relevant sources might reach statistical significance. The large number of groups examined reduces, but does not eliminate, the risk of such bias. The inability of our study to assess the effect of HLA-B*5701 screening on the relative efficacy of abacavir-lamivudine is a key example of this, and one which necessitated post-hoc analysis in an attempt at characterisation. The failure of some covariates to reach significance may also highlight which data are currently poorly reported in studies. We were able to source some non-published data from pharmaceutical sponsors, but very little from academic sponsors. For each subgroup analysis, population numbers changed, so comparisons between subgroups can only be inferred. For most studies, randomisation was not stratified by pre-treatment viral load. It is likely that variables not reported by viral loads strata (previous AIDS events, CD4 lymphocyte count, adherence, co-infection with viral hepatitis) can partly explain the differences seen between high and low viral load strata. This limitation does not affect other subgroup analyses. Finally, HIV viral load was used as the primary outcome measure, rather than a clinical endpoint. Our data identifies pre-treatment HIV-1 viral load as a determinant of efficacy, which should prompt a re-examination of its place in treatment guidelines, and also suggests that HIVinfected adults should initiate cART for plasma viral loads rising towards 100,000 copies/mL. Whether this difference is driven by lesser antiviral potency at high viral loads is unknown because of missing data, but requires further investigation. One possible option would be a prospective study comparing triple- and quadruple-drug combinations as initial therapy in high viral loads. The medium-term efficacy of initial cART alone remains
unsatisfactory, and prospective studies require longer follow-up and better reporting of adverse events and reasons for participantinitiated treatment cessation. As an ecological analysis of pooled outcomes, our findings are associations rather than causes, best applied to populations with demographics similar to our source data. Future analyses performed using data from individual participants are needed to allow specific causality to be inferred for the trends identified in this study.
Supporting Information Appendix S1
Supplementary post-hoc stratified analy-
ses. (DOC) Checklist S1 2009 PRISMA Checklist.
(DOC) Protocol S1 Statistical Analysis Plan.
(PDF)
Acknowledgments The authors acknowledge the contributions of Bristol Myers-Squibb, Gilead Sciences, MSD, ViiV Healthcare, Rebekah Puls and Kathy Petoumenos in providing unpublished pre-treatment, eligibility and outcome data.
Author Contributions Conceived and designed the experiments: FJL JA AC. Performed the experiments: FJL JA AC. Analyzed the data: FJL JA AC. Contributed reagents/materials/analysis tools: FJL JA AC. Wrote the paper: FJL JA AC.
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